mmocr/configs/textdet/psenet/psenet_r50_fpnf.py

62 lines
1.9 KiB
Python

model_poly = dict(
type='PSENet',
backbone=dict(
type='mmdet.ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=-1,
norm_cfg=dict(type='SyncBN', requires_grad=True),
init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50'),
norm_eval=True,
style='caffe'),
neck=dict(
type='FPNF',
in_channels=[256, 512, 1024, 2048],
out_channels=256,
fusion_type='concat'),
det_head=dict(
type='PSEHead',
in_channels=[256],
hidden_dim=256,
out_channel=7,
module_loss=dict(type='PSEModuleLoss'),
postprocessor=dict(type='PSEPostprocessor', text_repr_type='poly')),
data_preprocessor=dict(
type='TextDetDataPreprocessor',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
bgr_to_rgb=True,
pad_size_divisor=32))
model_quad = dict(
type='PSENet',
backbone=dict(
type='mmdet.ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=-1,
norm_cfg=dict(type='SyncBN', requires_grad=True),
norm_eval=True,
init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50'),
style='pytorch'),
neck=dict(
type='FPNF',
in_channels=[256, 512, 1024, 2048],
out_channels=256,
fusion_type='concat'),
det_head=dict(
type='PSEHead',
in_channels=[256],
hidden_dim=256,
out_channel=7,
module_loss=dict(type='PSEModuleLoss'),
postprocessor=dict(type='PSEPostprocessor', text_repr_type='quad')),
data_preprocessor=dict(
type='TextDetDataPreprocessor',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
bgr_to_rgb=True,
pad_size_divisor=32))